International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Powered ECG Signal Analyzer to Detect CVD Diseases |
|---|---|
| ABSTRACT | Cardiovascular diseases (CVDs) are among the leading causes of mortality worldwide, requiring early and accurate diagnosis to reduce health risks. Electrocardiogram (ECG) analysis is a widely used non-invasive technique for detecting heart abnormalities; however, manual interpretation is time-consuming and prone to human error. This paper presents an AI-powered ECG signal analyzer that automatically detects cardiovascular diseases using deep learning techniques. The proposed system preprocesses ECG signals, extracts relevant features, and classifies them into normal and abnormal categories using a convolutional neural network (CNN). The model is trained on labeled ECG datasets and evaluated using standard performance metrics such as accuracy, precision, recall, and F1-score. A user-friendly web interface is developed to allow users to upload ECG data and obtain real-time diagnostic predictions. Experimental results demonstrate that the proposed system achieves high accuracy and improves diagnostic efficiency, making it suitable for clinical assistance and educational applications. |
| AUTHOR | JEEVAN M O, MANOJ B C, MADIVALARA MAHANTESHA, BHOOMIKA N UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312120 |
| pdf/120_AI-Powered ECG Signal Analyzer to Detect CVD Diseases.pdf | |
| KEYWORDS | |
| References | 1) Y. LeCun, Y. Bengio, and G. Hinton, Deep knowledge, Nature, vol. 521, no. 7553, pp. 436 – 444, May 2015. 2) A. Hannun et al., Cardiologist- position arrhythmia discovery using convolutional neural networks, Nature Medicine, vol. 25, no. 1, pp. 65 – 69, Jan. 2019. 3) U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, and M. Adam, A deep convolutional neural network model to classify jiffs, Computers in Biology and Medicine, vol. 89, pp. 389 – 396, Oct. 2017. 4) S. Kiranyaz, T. Ince, and M. Gabbouj, Real- time case-specific ECG type by 1- D convolutional neural networks, IEEE Deals on Biomedical Engineering, vol. 63, no. 3, pp. 664 – 675, Mar. 2016. 5) G. B. Moody and R. G. Mark, The impact of the MIT-BIH Arrhythmia Database, IEEE Engineering in Medicine and Biology Magazine, vol. 20, no. 3, pp. 45 – 50, May – Jun. 2001. 6) P. Rajpurkar et al., Deep knowledge for cardiologist- position arrhythmia discovery from ECG signals, IEEE Access, vol. 8, pp. 203 – 213, 2020. 7) D. P. Kingma and J. Ba, Adam A system for stochastic optimization, in Proc. Int. Cone. Learning Representations (CLR), 2015. 8) M. Elgendi, Fast QRS discovery with an optimized knowledge- rested system, IEEE Deals on Biomedical Engineering, vol. 60, no. 4, pp. 1042 – 1052, Apr. 2013. 9) J. Zhang, X. Wang, and Y. Liu, ECG signal type using deep knowledge ways, Biomedical Signal Processing and Control, vol. 43, pp. 48 – 56, May 2018. 10) S. Shashikumar et al., Deep knowledge for automated ECG analysis A review, Journal of Possible spelling mistake found., vol. 57, pp. 41 – 49, Nov. 2019. |
